A spatial and temporal analysis of forest dynamics using Landsat time-series

Abstract

Understanding forest dynamics at the landscape scale is critical given the demands of sustainable forest management and climate change mitigation. This study proposes an approach for holistically characterising and analysing forest dynamics across large areas and long-time periods using information derived from Landsat time-series. To achieve this, we first developed a two-phase classification process to predictively map (1) disturbance and recovery levels and (2) disturbance causal agents for multiple detected disturbance events. The model explanatory data included a range of trajectory-based metrics derived from Landsat time-series, while model training and validation data were derived from a human-interpreted reference dataset. While previous studies have often described forest dynamics using some specific spectral change metrics, we demonstrated an ensemble approach to map disturbance and recovery trends (by treating them as a single metric) and to characterise not only abruptly occurring change events (e.g., clear-fell logging and wildfire) but also events of low severity (e.g., prescribed burning and selective logging). In addition, we adopted a space-time data cube concept to simultaneously report both newly detected disturbance events (detected disturbances) as well as events that have previously occurred but are ongoing (ongoing disturbances). This ongoing element of forest dynamics is often under-reported. The method was tested over 3.7 million ha of public land sclerophyll forests, where multiple disturbance events have occurred over the last 30 years (1987–2016). Our models of disturbance and recovery levels obtained overall accuracies of 78.6% and 72.3% for primary and secondary disturbance events, respectively. The overall accuracies for the models of disturbance causal agents were 80.7% and 73.0%, respectively. The data cube reported an average annual disturbance rate of 4.2% per year. This was dominated by newly detected disturbance (2.7% per year) as distinct from ongoing disturbance that was, however, considerable (1.5% per year). Our approach presented herein can improve the understanding of forest dynamics over long time periods and large areas and has potential for supporting land managers.